In HarrisQ/linearsdr: Linear Sufficient Reduction Methods

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Tuning OPCG

We can tune the bandwidth in the Outer Product of Canonical Gradients (OPCG) using the "supervised k-means"-like approach.

1. For a given $h$, we estimate $\hat \beta$ using some training set;
2. Construct the sufficient predictors for the given $h$ on some tuning/validation set, denote $\hat \beta^{\top} X^{(v)}$;
3. Pick $h$ that minimizes a k-means-like criterion;

Below is an interactive figure illustrating the methods on some synthetic data. We generate the data from five clusters that are labeled into 3 groups. By moving the slider, you will notice that the minimum of the criterion functions corresponds with very good separation of the labels.

HarrisQ/linearsdr documentation built on Nov. 29, 2022, 12:22 a.m.